In the prior art, such estimates and schedule and rate offer selections were typically made manually by employees of either the enterprise or the utility supplying a commodity, such as electric power, who were assigned this task.
In the case of manually produced estimates, operating costs were generally high. The cost of hiring and training the employees who produced the ad-hoc estimates typically makes such process prohibitively expensive for all but the largest sites. In addition, implementation cost is high. It is difficult for the estimators to determine the utility consumption of a scheduled resource, such as a machine or production line, without installing submetering equipment for the resource. Furthermore, the submeters had to be read manually or communication equipment has to be installed to read them automatically. This adds to the expense of doing the estimation in the manual manner. Accuracy is low and human error often makes the estimations themselves unreliable. Opportunity for integration within an enterprise using a manual system was limited. The ad-hoc estimates generally cannot be used to automate the business interactions of the enterprise and the utility.
With this type of manual operation, pportunity for trade remains limited. The ad-hoc estimates cannot generally be traded as an information product. Moreover, the overall market for manually obtained estimation is severely limited. Because the estimation process is so cumbersome, typically only those enterprises with the largest sites went through it at all.
Usefulness of such manually derived estimates outside the enterprise was limited. Because so few enterprises produced estimates, utilities had little opportunity to use the estimates of its customers to optimize its own operations.
The difficulty of producing reliable estimates has contributed to inelastic demand for utilities. Because most enterprises could not adapt their schedules to utility price structures, the price of the utility made little difference in instantaneous total consumption. In the deregulated electric power industry, this leads to spiraling prices in times of shortages and missed opportunities for profit in times of surpluses.
Background material is found in, for example, the following U.S. patents:    U.S. Pat. No. 3,602,703, POWER DEMAND PREDICTING CONTROL SYSTEM, issued Aug. 31, 1971 in the name of Polenz;    U.S. Pat. No. 3,789,201, SIMULATED LOAD FORECAST AND CONTROL APPARATUS, issued Jan. 29, 1974 in the name of Carpenter et al.;    U.S. Pat. No. 4,916,328, ADD/SHED LOAD CONTROL USING ANTICIPATORY PROCESSES, issued Apr. 10, 1990, in the name of Culp, III; and    U.S. Pat. No. 5,963,457, ELECTRICAL POWER DISTRIBUTION MONITORING SYSTEM AND METHOD.
See also the article: Handschin, E. and Doernemann, Ch., “Bus Load Modeling and Forecasting,” IEEE Trans. Power Systems, 3:2, May 1988, 627-633. The technique relies on the use of “normalized load curves” and thus applies only to wide-area forecasting.
While predicting utility consumption on low levels (machines, devices, loads, etc) from known device characterizations or patterns is known, the present invention discloses predicting utility consumption where no such patterns are known, and where the low-level scheduling of individual devices cannot be easily derived from the higher-level schedule.
While the predicting of utility consumption on very large scales (cities or states), using primarily unscheduled factors such as weather, is known, and while such an approach may generally be adequate for such large-scale prediction, it is herein recognized that for smaller, site-scale utility users, in which production and other schedules can heavily influence utility consumption, such a wide-scale statistical approach would not be sufficiently accurate.